Here are some issues with these: 1) because they aren't (as far as I know) "componentized", it's just a big black wall you toss over, and an answer comes back

Here are some issues with these: 1) because they aren't (as far as I know) "componentized", it's just a big black wall you toss over, and an answer comes back for these "emergent" mathematical abilities. 2) on that note, what is the correctness across the input range of the operation? Is that tested? 3) even if it is, these are evolving models, how often is it tested? What would make sense a bit is if the resolution indicates what "parts" of the LLM "brain" were used. For math, wouldn't it make sense that the LLM should identify the operation for most known formal math operations and instead service-invoke an actual hard math implementation, and in the answer identify that was what it did so you have some confidence in the answer? As others have pointed out, there is probably a lot of "caching" going on of answers. Effective... unless the cached answer is wrong, or the answer ISN'T cached and it delegates to some other combination of virtual neurons to produce ??? answer. So far ChatGPT is an impressive demonstration. It's good for generating food for thought, examples, or maybe alternatives, but I don't see myself using it for "answers" in any definitive way unless it tells me HOW it got that answer. And man, can the idiot fourth estate keep its hype bullshit stories away from this? The last thing an important thing like "general AI" needs is the general press and its abysmal horrid freak-show undercurrent and anti-intellectual bias to chop it up. Yeah, I know, no way. Ironically, ChatGPT may augur better science reporting in the future, because it will almost certainly author better articles that dumb uneducated "senior" writers (not interns nonononono) would.